Resourceful application and mechanism of oyster shell-microalgae synergistic system：Sustainable treatment of harsh low carbon nitrogen ratio actual wastewater.

Microalgal technology holds great promise for both low C/N wastewater treatment and resource recovery simultaneously. Nevertheless, the advancement of microalgal technology is hindered by its reduced nitrogen removal efficiency in low C/N ratio wastewater. In this work, microalgae and waste oyster shells were combined to achieve a total inorganic nitrogen removal efficiency of 93.85% at a rate of 2.05 mg L-1 h-1 in low C/N wastewater. Notably, over four cycles of oyster shell reuse, the reactor achieved an average 85% ammonia nitrogen removal extent, with a wastewater treatment cost of only $0.092/ton. Moreover, microbial community analysis during the reuse of oyster shells revealed the critical importance of timely replacement in inhibiting the growth of non-functional bacteria (Poterioochromonas_malhamensi). The work demonstrated that the oyster shell - microalgae system provides a time- and cost-saving, environmental approach for the resourceful treatment of harsh low C/N wastewater.

Research on Guidance Methods of Digital Twin Robotic Arms Based on User Interaction Experience Quantification.

The evolution of the manufacturing sector coupled with advancements in digital twin technology has precipitated the extensive integration of digital twin robotic arms within the industrial domain. Notwithstanding this trend, there exists a paucity of studies examining the interaction of these robotic arms in virtual reality (VR) contexts from the user's standpoint. This paper delves into the virtual interaction of digital twin robotic arms by concentrating on effective guidance methodologies for the input of their target motion trajectories. Such a focus is pivotal to optimize input precision and efficiency, thus contributing to research on the virtual interaction interfaces of these robotic arms. During empirical evaluations, metrics related to human-machine interaction, such as objective operational efficiency, precision, and subjective workload, were meticulously quantified. Moreover, the influence of disparate guidance methods on the interaction experience of digital twin robotic arms and their corresponding scenarios was investigated. Consequent findings offer pivotal insights regarding the efficacy of these guidance methods across various scenarios, thereby serving as an invaluable guide for future endeavors aiming to bolster interactive experiences in devices akin to digital twin robotic arms.

A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs.

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.

A Dual Distribution Control Method for Multi-Power Components Energy Output of 4WD Electric Vehicles.

Energy management strategies are vitally important to give full play to the energy-saving of the four-wheel drive electric vehicle (4WD EV). The cooperative output of multi-power components is involved in the process of driving and braking energy recovery of 4WD EV. This paper proposes a novel energy management strategy of dual equivalent consumption minimization strategy (D-ECMS) to improve the economy of the vehicle. According to the different driving and braking states of the vehicle, D-ECMS can realize the proportional control of the energy cooperative output among the multi-power components. Under the premise of satisfying the dynamic performance of the vehicle, the operating points of the power components are distributed more in the high-efficiency range, and the economy and driving range of the vehicle are optimized. In order to achieve the effectiveness of D-ECMS, MATLAB/Simulink is used to realize the simulation of the vehicle. Compared with the rule-based strategy, the economy of D-ECMS increased by 4.35%.

An Equivalent Consumption Minimization Strategy for a Parallel Plug-In Hybrid Electric Vehicle Based on an Environmental Perceiver.

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional ECMS.

State-of-Health Prediction Using Transfer Learning and a Multi-Feature Fusion Model.

Existing data-driven technology for prediction of state of health (SOH) has insufficient feature extraction capability and limited application scope. To deal with this challenge, this paper proposes a battery SOH prediction model based on multi-feature fusion. The model is based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). The CNN can learn the cycle features in the battery data, the LSTM can learn the aging features of the battery over time, and regression prediction can be made through the full-connection layer (FC). In addition, for the aging differences caused by different battery operating conditions, this paper introduces transfer learning (TL) to improve the prediction effect. Across cycle data of the same battery under 12 different charging conditions, the fusion model in this paper shows higher prediction accuracy than with either LSTM and CNN in isolation, reducing RMSPE by 0.21% and 0.19%, respectively.

SGGformer: Shifted Graph Convolutional Graph-Transformer for Traffic Prediction.

Accurate traffic prediction is significant in intelligent cities' safe and stable development. However, due to the complex spatiotemporal correlation of traffic flow data, establishing an accurate traffic prediction model is still challenging. Aiming to meet the challenge, this paper proposes SGGformer, an advanced traffic grade prediction model which combines a shifted window operation, a multi-channel graph convolution network, and a graph Transformer network. Firstly, the shifted window operation is used for coarsening the time series data, thus, the computational complexity can be reduced. Then, a multi-channel graph convolutional network is adopted to capture and aggregate the spatial correlations of the roads in multiple dimensions. Finally, the improved graph Transformer based on the advanced Transformer model is proposed to extract the long-term temporal correlation of traffic data effectively. The prediction performance is evaluated by using actual traffic datasets, and the test results show that the SGGformer proposed exceeds the state-of-the-art baseline.

A Dual-Adaptive Equivalent Consumption Minimization Strategy for 4WD Plug-In Hybrid Electric Vehicles.

Energy management strategies are vitally important to give full play to energy-saving four-wheel-drive plug-in hybrid electric vehicles (4WD PHEV). This paper proposes a novel dual-adaptive equivalent consumption minimization strategy (DA-ECMS) for the complex multi-energy system in the 4WD PHEV. In this strategy, management of the multi-energy system is optimized by introducing the categories of future driving conditions to adjust the equivalent factors and improving the adaptability and economy of driving conditions. Firstly, a self-organizing neural network (SOM) and grey wolf optimizer (GWO) are adopted to classify the driving condition categories and optimize the multi-dimensional equivalent factors offline. Secondly, SOM is adopted to identify driving condition categories and the multi-dimensional equivalent factors are matched. Finally, the DA-ECMS completes the multi-energy optimization management of the front axle multi-energy sources and the electric driving system and releases the energy-saving potential of the 4WD PHEV. Simulation results show that, compared with the rule-based strategy, the economy in the DA-ECMS is improved by 13.31%.

State of Health Prediction of Lithium-Ion Battery Based on Deep Dilated Convolution.

A battery's charging data include the timing information with respect to the charge. However, the existing State of Health (SOH) prediction methods rarely consider this information. This paper proposes a dilated convolution-based SOH prediction model to verify the influence of charging timing information on SOH prediction results. The model uses holes to fill in the standard convolutional kernel in order to expand the receptive field without adding parameters, thereby obtaining a wider range of charging timing information. Experimental data from six batteries of the same battery type were used to verify the model's effectiveness under different experimental conditions. The proposed method is able to accurately predict the battery SOH value in any range of voltage input through cross-validation, and the SDE (standard deviation of the error) is at least 0.28% lower than other methods. In addition, the influence of the position and length of the range of input voltage on the model's prediction ability is studied as well. The results of our analysis show that the proposed method is robust to different sampling positions and different sampling lengths of input data, which solves the problem of the original data being difficult to obtain due to the uncertainty of charging-discharging behaviour in actual operation.

Expression, purification and characterization of a recombinant antimicrobial peptide Hispidalin in Pichia pastoris.

Hispidalin is a novel antimicrobial peptide isolated from the seeds of Benincasa hispida and is reported to have broad antimicrobial activity against various bacterial and fungal pathogens. To produce significant amounts of Hispidalin, a recombinant Hispidalin with an N-terminal 6 × His tag and an enterokinase sequence, for the first time, was successfully expressed in Escherichia coli or Pichia pastoris cell factory. Results showed that the E. coli-derived recombinant Hispidalin did not show any antimicrobial activity against all the tested strains, whereas the P. pastoris-derived recombinant Hispidalin (rHispidalin) showed a broad antibacterial spectrum against five pathogenic bacteria of both Gram-negative and Gram-positive. rHispidalin also has bactericidal activity and completely killed all of the Staphylococcus aureus within 40 min. Additionally, rHispidalin showed a broad range of thermostability and pH stability, and a hemolytic activity of less than 2% even at a concentration of 300 µg/ml; it was resistant to trypsin and proteinase K, but was moderately sensitive to pepsin and papain. Moreover, rHispidalin effectively permeabilized the cytoplasmic membrane and disrupted the morphology of targeted bacterial cells. After an initial optimization was performed, the amount of rHispidalin accumulation could reach as high as 98.6 µg/ml. These results indicate that Hispidalin could be produced on a large scale by P. pastoris and has a great potential to be utilized as a new antibacterial agent for further development.

Progress and summary of reinforcement learning on energy management of MPS-EV.

The escalating environmental concerns and energy crisis caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) integrate various clean energy systems to enhance the powertrain efficiency. The energy management strategy (EMS) is plays a pivotal role for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement Learning (RL) has emerged as an effective methodology for EMS development, attracting continuous attention and research. However, a systematic analysis of the design elements of RL-based EMS is currently lacking. This paper addresses this gap by presenting a comprehensive analysis of current research on RL-based EMS (RL-EMS) and summarizing its design elements. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. It highlights the contributions of advanced algorithms to training effectiveness, provides a detailed analysis of perception and control schemes, classifies different reward function settings, and elucidates the roles of innovative training methods. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Potential development directions are suggested for implementing advanced artificial intelligence (AI) solutions in EMS.

A synthetic digital city dataset for robustness and generalisation of depth estimation models.

Existing monocular depth estimation driving datasets are limited in the number of images and the diversity of driving conditions. The images of datasets are commonly in a low resolution and the depth maps are sparse. To overcome these limitations, we produce a Synthetic Digital City Dataset (SDCD) which was collected under 6 different weather driving conditions, and 6 common adverse perturbations caused by the data transmission. SDCD provides a total of 930 K high-resolution RGB images and corresponding perfect observed depth maps. The evaluation shows that depth estimation models which are trained on SDCD provide a clearer, smoother, and more precise long-range depth estimation compared to those trained on one of the best-known driving datasets KITTI. Moreover, we provide a benchmark to investigate the performance of depth estimation models in different adverse driving conditions. Instead of collecting data from the real world, we generate the SDCD under severe driving conditions with perfect observed data in the digital world, enhancing depth estimation for autonomous driving.

Potential auto-driving threat: Universal rain-removal attack.

Severe weather conditions pose a significant challenge for computer vision algorithms in autonomous driving applications, particularly regarding robustness. Image rain-removal algorithms have emerged as a potential solution by leveraging the power of neural networks to restore rain-free backgrounds in images. However, existing research overlooks the vulnerability concerns in neural networks, which exposes a potential threat to the intelligent perception of autonomous vehicles in rainy conditions. This paper proposes a universal rain-removal attack (URA) that exploits the vulnerability of image rain-removal algorithms. By generating a non-additive spatial perturbation, URA significantly diminishes scene restoration similarity and image quality. The imperceptible and generic perturbation employed by URA makes it a crucial tool for vulnerability detection in image rain-removal algorithms and a potential real-world AI attack method. Experimental results demonstrate that URA can reduce scene repair capability by 39.5% and image generation quality by 26.4%, effectively targeting state-of-the-art rain-removal algorithms.

Achieving stable anammox process and revealing shift of bacteria during the start-up in landfill leachate treatment.

Partial nitrification-anammox (PN/A) is an energy-efficient technology for nitrogen removal in landfill leachate treatment. Numerous studies have reported successful implementation of the PN/A process and its stable operation under laboratory conditions. One of the primary challenges in PN/A engineering applications is the mass of the seed sludge required for start-up. This study examined the PN/A using a sequence batch reactor (SBR) inoculating a common mixture to treat landfill leachate. After 70 days of operation, the system successfully realized a one-stage PN/A process and maintained a stable ammonium NH 4 + $$ \left({NH}_4^{+}\right) $$ removal efficiency of 97.65% ± 1%, where the effluent of NH 4 + $$ {NH}_4^{+} $$ and nitrate ( NO 3 - $$ {NO}_3^{-} $$ ) were less than 4 ± 1.5 mg L-1 and 10 mg L-1 . In addition, the relative abundances of Ca. Kuenenia and Ca. Brocadia, which are typical anaerobic ammonia-oxidizing bacteria (AnAOB), increased from 0.08% to 3.99% (70 days) and 0.01% to 0.45%, respectively. The relative abundances of ammonia-oxidizing bacteria (AOB) Nitrosomonas and Nitrosospira increased from 0.9% to 2.89% and 0.007% to 0.1% (70 days), respectively. Both AnAOB and AOB are important niches of the system. PRACTITIONER POINTS: The research realized PN/A rapidly by inoculating common mixture sludge. The experiment successfully enriched AnAOB from 0.09% to 3.89% within 70 days. The article revealing the ecological roles of AOB and AnAOB in the landfill leachate treatment.

Research and Application of the Data Mining Technology in Economic Intelligence System.

In the context of the rapid development of the modern economy, information is particularly important in the economic field, and information determines the decision-making of enterprises. Therefore, how to quickly dig out information that is beneficial to the enterprise has become a crucial issue. This topic applies data mining technology to economic intelligent systems and obtains the data object model of economic intelligent systems through the integration of information. This article analyzes the interrelationship between its objects on this basis and uses data mining-related methods to mine it. The establishment of economic intelligence systems not only involves the establishment of mathematical models of economic systems, but also includes research on the algorithms applied to them. How to apply an algorithm to quickly and accurately extract the required economic intelligence domain information from the potential information in the database, or to provide a method to find the best solution, involves the use of association rules and classification prediction methods. The application of data mining algorithms can be used to study the application of economic intelligence systems. This paper develops and designs an economic intelligence information database and realizes the economic intelligence system on this basis, and realizes the research results. Finally, this paper has tested the dataset, and the results show that the classification accuracy of this algorithm is 2.64% higher than that of the ID3 algorithm.

Cyclization of Alkyne-Azide with Isonitrile/CO via Self-Relay Rhodium Catalysis.

A self-relay rhodium(I)-catalyzed cyclization of alkyne-azides with two σ-donor/π-acceptor ligands (isonitriles and CO) to form sequentially multiple-fused heterocycle systems via tandem nitrene transformation and aza-Pauson-Khand cyclization has been developed. In this approach, an intriguing chemoselective insertion process of isonitriles superior to CO was observed. This reaction provides an alternative strategy to synthesize functionalized pyrrolo[2,3-b]indole scaffolds.

Effect of Annealing Temperature on the Mechanical and Corrosion Behavior of a Newly Developed Novel Lean Duplex Stainless Steel.

The effect of annealing temperature (1000-1150 °C) on the microstructure evolution, mechanical properties, and pitting corrosion behavior of a newly developed novel lean duplex stainless steel with 20.53Cr-3.45Mn-2.08Ni-0.17N-0.31Mo was studied by means of optical metallographic microscopy (OMM), scanning electron microscopy (SEM), magnetic force microscopy (MFM), scanning Kelvin probe force microscopy (SKPFM), energy dispersive X-ray spectroscopy (EDS), uniaxial tensile tests (UTT), and potentiostatic critical pitting temperature (CPT). The results showed that tensile and yield strength, as well as the pitting corrosion resistance, could be degraded with annealing temperature increasing from 1000 up to 1150 °C. Meanwhile, the elongation at break reached the maximum of 52.7% after annealing at 1050 °C due to the effect of martensite transformation induced plasticity (TRIP). The localized pitting attack preferentially occurred at ferrite phase, indicating that the ferrite phase had inferior pitting corrosion resistance as compared to the austenite phase. With increasing annealing temperature, the pitting resistance equivalent number (PREN) of ferrite phase dropped, while that of the austenite phase rose. Additionally, it was found that ferrite possessed a lower Volta potential than austenite phase. Moreover, the Volta potential difference between ferrite and austenite increased with the annealing temperature, which was well consistent with the difference of PREN.

Image processing for a tactile/vision substitution system using digital CNN.

In view of the parallel processing and easy implementation properties of CNN, we propose to use digital CNN as the image processor of a tactile/vision substitution system (TVSS). The digital CNN processor is used to execute the wavelet down-sampling filtering and the half-toning operations, aiming to extract important features from the images. A template combination method is used to embed the two image processing functions into a single CNN processor. The digital CNN processor is implemented on an intellectual property (IP) and is implemented on a XILINX VIRTEX II 2000 FPGA board. Experiments are designated to test the capability of the CNN processor in the recognition of characters and human subjects in different environments. The experiments demonstrates impressive results, which proves the proposed digital CNN processor a powerful component in the design of efficient tactile/vision substitution systems for the visually impaired people.